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Special Issue Information

Dear Colleagues,

Labelling user data is a central part of the design and evaluation of sensors and sensor-based systems that aim to support the user through situation-aware reasoning. It is essential, both in designing and training a sensor-based system to recognize and reason about the situation, either through the design of new sensors, the definition of suitable observation and situation models in knowledge-driven applications, or though the preparation of training data for learning tasks in data-driven models. Hence, the quality of annotations can have a significant impact on the performance of the derived systems. Labelling is also vital for validating and quantifying the performance of sensors and sensor-based applications as well as for selecting the best performing sensor setup and configuration.

With sensor-based systems relying increasingly on large datasets with multiple sensors, the process of data labelling is becoming a major concern for the community.

To address these problems, this Special Issue contains selected papers from the International Workshop on Annotation of useR Data for UbiquitOUs Systems (ARDUOUS)(2017/2018) with focus on:

1) intelligent and interactive tools and automated methods for annotating large sensor datasets.2) the role and impact of annotations in designing sensor-based applications,3) the process of labelling, and the requirements to produce high quality annotations, especially in the context of large sensor datasets.

In addition, we are looking for outstanding submissions, which will extend the state-of-the-art in annotation for sensor-based systems. The scope of the issue includes but is not limited to:

- methods and intelligent tools for annotating sensor data - processes of and best practices in annotating sensor data - annotation methods and tools for sensor setup and configuration - sensors and sensor-based methods and practices towards an automation of the annotation - improving and evaluating the annotation quality for better sensor interpretation - ethical issues concerning the collection and annotation of sensor data - beyond the labels: ontologies for semantic annotation of sensor data - high-quality and re-usable annotation for publicly available sensor datasets - impact of annotation on a sensor-based system's performance - building classifier models that are capable of dealing with multiple (noisy) annotations and/or making use of taxonomies/ontologies - the potential value of incorporating modelling of the annotators into predictive models

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access monthly journal published by MDPI.

Cyclic signals are an intrinsic part of daily life, such as human motion and heart activity. The detailed analysis of them is important for clinical applications such as pathological gait analysis and for sports applications such as performance analysis. Labeled training data for

Cyclic signals are an intrinsic part of daily life, such as human motion and heart activity. The detailed analysis of them is important for clinical applications such as pathological gait analysis and for sports applications such as performance analysis. Labeled training data for algorithms that analyze these cyclic data come at a high annotation cost due to only limited annotations available under laboratory conditions or requiring manual segmentation of the data under less restricted conditions. This paper presents a smart annotation method that reduces this cost of labeling for sensor-based data, which is applicable to data collected outside of strict laboratory conditions. The method uses semi-supervised learning of sections of cyclic data with a known cycle number. A hierarchical hidden Markov model (hHMM) is used, achieving a mean absolute error of 0.041 ± 0.020 s relative to a manually-annotated reference. The resulting model was also used to simultaneously segment and classify continuous, ‘in the wild’ data, demonstrating the applicability of using hHMM, trained on limited data sections, to label a complete dataset. This technique achieved comparable results to its fully-supervised equivalent. Our semi-supervised method has the significant advantage of reduced annotation cost. Furthermore, it reduces the opportunity for human error in the labeling process normally required for training of segmentation algorithms. It also lowers the annotation cost of training a model capable of continuous monitoring of cycle characteristics such as those employed to analyze the progress of movement disorders or analysis of running technique.
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